Subunit Redundancy Within the Nurd Complex Ensures Fidelity of ES Cell Lineage
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Single Nucleotide Variants in Metastasis-Related Genes Are
View metadata, citation and similar papers at core.ac.uk brought to you by CORE HHS Public Access provided by CDC Stacks Author manuscript Author ManuscriptAuthor Manuscript Author Mol Carcinog Manuscript Author . Author manuscript; Manuscript Author available in PMC 2018 March 01. Published in final edited form as: Mol Carcinog. 2017 March ; 56(3): 1000–1009. doi:10.1002/mc.22565. Single nucleotide variants in metastasis-related genes are associated with breast cancer risk, by lymph node involvement and estrogen receptor status, in women with European and African ancestry Michelle R. Roberts1,2,3, Lara E. Sucheston-Campbell4, Gary R. Zirpoli5, Michael Higgins6, Jo L. Freudenheim3, Elisa V. Bandera7, Christine B. Ambrosone2, and Song Yao2 1Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 2Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY 3Department of Epidemiology and Environmental Health, University at Buffalo, Buffalo, NY 4Division of Pharmacy Practice and Science, The Ohio State University, Columbus, OH 5Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 6Department of Molecular and Cellular Biology, Roswell Park Cancer Institute, Buffalo, NY 7Rutgers Cancer Institute of New Jersey, New Brunswick, NJ Abstract Background—Single nucleotide polymorphisms (SNPs) in pathways influencing lymph node (LN) metastasis and estrogen receptor (ER) status in breast cancer may partially explain inter- patient variability in prognosis. We examined 154 SNPs in 12 metastasis-related genes for associations with breast cancer risk, stratified by LN and ER status, in European-American (EA) and African-American (AA) women. Methods—2,671 women enrolled in the Women’s Circle of Health Study were genotyped. -
A Gene Regulatory Network Armature for T Lymphocyte Specification
Supporting Information Georgescu et al. 10.1073/pnas.0806501105 SI Text orientation in the multidimensional space, while the relative magnitude of gene expression variation is still visible on the 2D Computational Methods pls projection. All of the 2D visualizations included use the Partial Least Squares (PLS) Analysis of Normal Developmental Gene above transformations. Expression Change. PLS regression analysis was performed to find The lengths of the DN-stage vectors in the pls principal axis a hyper plane of reduced dimensionality relating the expression space indicate the extent to which these axes are explanatory of variation of the studied genes to the developmental stages DN1, the corresponding DN state. Because in the full multidimen- DN2, DN3A, DN3B, and DN4. PLS begins by characterizing the sional space this length is the same for all five DN stages, the five developmental stages DN1–DN4 as orthogonal axes in length of these vectors in the 2D projection shows the relative five-dimensional space. Each of the assayed genes is represented extent to which the DN stages are represented in the projection. as a point in this space, such that the gene’s coordinate along Long vectors indicate DN stages well represented in the projec- each axis corresponds to its expression level at the corresponding tion. In the multidimensional space, the cosine of the angle developmental stage. Genes with similar expression changes between the vectors corresponding to two variables (genes during development will tend to form clusters in this space, and/or DN stages) gives the correlation between the two vari- whereas genes with distinctly different expression profiles will ables. -
Longitudinal Study of Leukocyte DNA Methylation and Biomarkers for Cancer Risk in Older Adults
bioRxiv preprint doi: https://doi.org/10.1101/597666; this version posted April 3, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Longitudinal Study of Leukocyte DNA Methylation and 2 Biomarkers for Cancer Risk in Older Adults 3 Alexandra H. Bartlett1, Jane W Liang1, Jose Vladimir Sandoval-Sierra1, Jay H 4 Fowke 1, Eleanor M Simonsick2, Karen C Johnson1, Khyobeni Mozhui1* 5 1Department of Preventive Medicine, University of Tennessee Health Science 6 Center, Memphis, Tennessee, USA 7 2Intramural Research Program, National Institute on Aging, Baltimore Maryland, 8 USA 9 AHB: [email protected]; JWL: [email protected]; JVSS: 10 [email protected]; JHF: [email protected]; EMS: [email protected]; 11 KCJ: [email protected]; KM: [email protected] 12 *Corresponding author: Khyobeni Mozhui 13 14 15 16 17 18 1 bioRxiv preprint doi: https://doi.org/10.1101/597666; this version posted April 3, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 19 Abstract 20 Background: Changes in DNA methylation over the course of life may provide 21 an indicator of risk for cancer. We explored longitudinal changes in CpG 22 methylation from blood leukocytes, and likelihood of a future cancer diagnosis. -
Longitudinal Study of Leukocyte DNA Methylation and Biomarkers for Cancer Risk in Older Adults Alexandra H
Bartlett et al. Biomarker Research (2019) 7:10 https://doi.org/10.1186/s40364-019-0161-3 RESEARCH Open Access Longitudinal study of leukocyte DNA methylation and biomarkers for cancer risk in older adults Alexandra H. Bartlett1, Jane W. Liang1, Jose Vladimir Sandoval-Sierra1, Jay H. Fowke1, Eleanor M. Simonsick2, Karen C. Johnson1 and Khyobeni Mozhui1* Abstract Background: Changes in DNA methylation over the course of life may provide an indicator of risk for cancer. We explored longitudinal changes in CpG methylation from blood leukocytes, and likelihood of future cancer diagnosis. Methods: Peripheral blood samples were obtained at baseline and at follow-up visit from 20 participants in the Health, Aging and Body Composition prospective cohort study. Genome-wide CpG methylation was assayed using the Illumina Infinium Human MethylationEPIC (HM850K) microarray. Results: Global patterns in DNA methylation from CpG-based analyses showed extensive changes in cell composition over time in participants who developed cancer. By visit year 6, the proportion of CD8+ T-cells decreased (p-value = 0. 02), while granulocytes cell levels increased (p-value = 0.04) among participants diagnosed with cancer compared to those who remained cancer-free (cancer-free vs. cancer-present: 0.03 ± 0.02 vs. 0.003 ± 0.005 for CD8+ T-cells; 0.52 ± 0. 14 vs. 0.66 ± 0.09 for granulocytes). Epigenome-wide analysis identified three CpGs with suggestive p-values ≤10− 5 for differential methylation between cancer-free and cancer-present groups, including a CpG located in MTA3, agene linked with metastasis. At a lenient statistical threshold (p-value ≤3×10− 5), the top 10 cancer-associated CpGs included a site near RPTOR that is involved in the mTOR pathway, and the candidate tumor suppressor genes REC8, KCNQ1,andZSWIM5. -
Supplemental Materials ZNF281 Enhances Cardiac Reprogramming
Supplemental Materials ZNF281 enhances cardiac reprogramming by modulating cardiac and inflammatory gene expression Huanyu Zhou, Maria Gabriela Morales, Hisayuki Hashimoto, Matthew E. Dickson, Kunhua Song, Wenduo Ye, Min S. Kim, Hanspeter Niederstrasser, Zhaoning Wang, Beibei Chen, Bruce A. Posner, Rhonda Bassel-Duby and Eric N. Olson Supplemental Table 1; related to Figure 1. Supplemental Table 2; related to Figure 1. Supplemental Table 3; related to the “quantitative mRNA measurement” in Materials and Methods section. Supplemental Table 4; related to the “ChIP-seq, gene ontology and pathway analysis” and “RNA-seq” and gene ontology analysis” in Materials and Methods section. Supplemental Figure S1; related to Figure 1. Supplemental Figure S2; related to Figure 2. Supplemental Figure S3; related to Figure 3. Supplemental Figure S4; related to Figure 4. Supplemental Figure S5; related to Figure 6. Supplemental Table S1. Genes included in human retroviral ORF cDNA library. Gene Gene Gene Gene Gene Gene Gene Gene Symbol Symbol Symbol Symbol Symbol Symbol Symbol Symbol AATF BMP8A CEBPE CTNNB1 ESR2 GDF3 HOXA5 IL17D ADIPOQ BRPF1 CEBPG CUX1 ESRRA GDF6 HOXA6 IL17F ADNP BRPF3 CERS1 CX3CL1 ETS1 GIN1 HOXA7 IL18 AEBP1 BUD31 CERS2 CXCL10 ETS2 GLIS3 HOXB1 IL19 AFF4 C17ORF77 CERS4 CXCL11 ETV3 GMEB1 HOXB13 IL1A AHR C1QTNF4 CFL2 CXCL12 ETV7 GPBP1 HOXB5 IL1B AIMP1 C21ORF66 CHIA CXCL13 FAM3B GPER HOXB6 IL1F3 ALS2CR8 CBFA2T2 CIR1 CXCL14 FAM3D GPI HOXB7 IL1F5 ALX1 CBFA2T3 CITED1 CXCL16 FASLG GREM1 HOXB9 IL1F6 ARGFX CBFB CITED2 CXCL3 FBLN1 GREM2 HOXC4 IL1F7 -
Contribution of Enhancer-Driven and Master-Regulator Genes to Autoimmune Disease Revealed Using Functionally Informed SNP-To-Gene Linking Strategies Kushal K
bioRxiv preprint doi: https://doi.org/10.1101/2020.09.02.279059; this version posted March 31, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Contribution of enhancer-driven and master-regulator genes to autoimmune disease revealed using functionally informed SNP-to-gene linking strategies Kushal K. Dey1, Steven Gazal1, Bryce van de Geijn 1,3, Samuel Sungil Kim 1,4, Joseph Nasser5, Jesse M. Engreitz5, Alkes L. Price 1,2,5 1 Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA 2 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 3 Genentech, South San Francisco, CA 4 Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 5 Broad Institute of MIT and Harvard, Cambridge, MA, USA Abstract Gene regulation is known to play a fundamental role in human disease, but mechanisms of regulation vary greatly across genes. Here, we explore the con- tributions to disease of two types of genes: genes whose regulation is driven by enhancer regions as opposed to promoter regions (Enhancer-driven) and genes that regulate many other genes in trans (Master-regulator). We link these genes to SNPs using a comprehensive set of SNP-to-gene (S2G) strategies and apply stratified LD score regression to the resulting SNP annotations to draw three main conclusions about 11 autoimmune diseases and blood cell traits (average Ncase=13K across 6 autoimmune diseases, average N=443K across 5 blood cell traits). -
Supplementary File 2A Revised
Supplementary file 2A. Differentially expressed genes in aldosteronomas compared to all other samples, ranked according to statistical significance. Missing values were not allowed in aldosteronomas, but to a maximum of five in the other samples. Acc UGCluster Name Symbol log Fold Change P - Value Adj. P-Value B R99527 Hs.8162 Hypothetical protein MGC39372 MGC39372 2,17 6,3E-09 5,1E-05 10,2 AA398335 Hs.10414 Kelch domain containing 8A KLHDC8A 2,26 1,2E-08 5,1E-05 9,56 AA441933 Hs.519075 Leiomodin 1 (smooth muscle) LMOD1 2,33 1,3E-08 5,1E-05 9,54 AA630120 Hs.78781 Vascular endothelial growth factor B VEGFB 1,24 1,1E-07 2,9E-04 7,59 R07846 Data not found 3,71 1,2E-07 2,9E-04 7,49 W92795 Hs.434386 Hypothetical protein LOC201229 LOC201229 1,55 2,0E-07 4,0E-04 7,03 AA454564 Hs.323396 Family with sequence similarity 54, member B FAM54B 1,25 3,0E-07 5,2E-04 6,65 AA775249 Hs.513633 G protein-coupled receptor 56 GPR56 -1,63 4,3E-07 6,4E-04 6,33 AA012822 Hs.713814 Oxysterol bining protein OSBP 1,35 5,3E-07 7,1E-04 6,14 R45592 Hs.655271 Regulating synaptic membrane exocytosis 2 RIMS2 2,51 5,9E-07 7,1E-04 6,04 AA282936 Hs.240 M-phase phosphoprotein 1 MPHOSPH -1,40 8,1E-07 8,9E-04 5,74 N34945 Hs.234898 Acetyl-Coenzyme A carboxylase beta ACACB 0,87 9,7E-07 9,8E-04 5,58 R07322 Hs.464137 Acyl-Coenzyme A oxidase 1, palmitoyl ACOX1 0,82 1,3E-06 1,2E-03 5,35 R77144 Hs.488835 Transmembrane protein 120A TMEM120A 1,55 1,7E-06 1,4E-03 5,07 H68542 Hs.420009 Transcribed locus 1,07 1,7E-06 1,4E-03 5,06 AA410184 Hs.696454 PBX/knotted 1 homeobox 2 PKNOX2 1,78 2,0E-06 -
Novel Insights Into the Thaumarchaeota in the Deepest Oceans: Their Metabolism and Potential Adaptation Mechanisms
Zhong et al. Microbiome (2020) 8:78 https://doi.org/10.1186/s40168-020-00849-2 RESEARCH Open Access Novel insights into the Thaumarchaeota in the deepest oceans: their metabolism and potential adaptation mechanisms Haohui Zhong1,2, Laura Lehtovirta-Morley3, Jiwen Liu1,2, Yanfen Zheng1, Heyu Lin1, Delei Song1, Jonathan D. Todd3, Jiwei Tian4 and Xiao-Hua Zhang1,2,5* Abstract Background: Marine Group I (MGI) Thaumarchaeota, which play key roles in the global biogeochemical cycling of nitrogen and carbon (ammonia oxidizers), thrive in the aphotic deep sea with massive populations. Recent studies have revealed that MGI Thaumarchaeota were present in the deepest part of oceans—the hadal zone (depth > 6000 m, consisting almost entirely of trenches), with the predominant phylotype being distinct from that in the “shallower” deep sea. However, little is known about the metabolism and distribution of these ammonia oxidizers in the hadal water. Results: In this study, metagenomic data were obtained from 0–10,500 m deep seawater samples from the Mariana Trench. The distribution patterns of Thaumarchaeota derived from metagenomics and 16S rRNA gene sequencing were in line with that reported in previous studies: abundance of Thaumarchaeota peaked in bathypelagic zone (depth 1000–4000 m) and the predominant clade shifted in the hadal zone. Several metagenome-assembled thaumarchaeotal genomes were recovered, including a near-complete one representing the dominant hadal phylotype of MGI. Using comparative genomics, we predict that unexpected genes involved in bioenergetics, including two distinct ATP synthase genes (predicted to be coupled with H+ and Na+ respectively), and genes horizontally transferred from other extremophiles, such as those encoding putative di-myo-inositol-phosphate (DIP) synthases, might significantly contribute to the success of this hadal clade under the extreme condition. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
A Set of Regulatory Genes Co-Expressed in Embryonic Human Brain Is Implicated in Disrupted Speech Development
Molecular Psychiatry https://doi.org/10.1038/s41380-018-0020-x ARTICLE A set of regulatory genes co-expressed in embryonic human brain is implicated in disrupted speech development 1 1 1 2 3 Else Eising ● Amaia Carrion-Castillo ● Arianna Vino ● Edythe A. Strand ● Kathy J. Jakielski ● 4,5 6 7 8 9 Thomas S. Scerri ● Michael S. Hildebrand ● Richard Webster ● Alan Ma ● Bernard Mazoyer ● 1,10 4,5 6,11 6,12 13 Clyde Francks ● Melanie Bahlo ● Ingrid E. Scheffer ● Angela T. Morgan ● Lawrence D. Shriberg ● Simon E. Fisher 1,10 Received: 22 September 2017 / Revised: 3 December 2017 / Accepted: 2 January 2018 © The Author(s) 2018. This article is published with open access Abstract Genetic investigations of people with impaired development of spoken language provide windows into key aspects of human biology. Over 15 years after FOXP2 was identified, most speech and language impairments remain unexplained at the molecular level. We sequenced whole genomes of nineteen unrelated individuals diagnosed with childhood apraxia of speech, a rare disorder enriched for causative mutations of large effect. Where DNA was available from unaffected parents, CHD3 SETD1A WDR5 fi 1234567890();,: we discovered de novo mutations, implicating genes, including , and . In other probands, we identi ed novel loss-of-function variants affecting KAT6A, SETBP1, ZFHX4, TNRC6B and MKL2, regulatory genes with links to neurodevelopment. Several of the new candidates interact with each other or with known speech-related genes. Moreover, they show significant clustering within a single co-expression module of genes highly expressed during early human brain development. This study highlights gene regulatory pathways in the developing brain that may contribute to acquisition of proficient speech. -
Metastasis-Associated Protein 2 Represses NF-Ƙb to Reduce Lung Tumor Growth And
Author Manuscript Published OnlineFirst on August 14, 2020; DOI: 10.1158/0008-5472.CAN-20-1158 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. 1 Metastasis-associated protein 2 represses NF-ƙB to reduce lung tumor growth and 2 inflammation 3 Nefertiti El-Nikhely1#, Annika Karger1, Poonam Sarode1, Indrabahadur Singh1§, Andreas 4 Weigert2, Astrid Wietelmann1, Thorsten Stiewe3, Reinhard Dammann4, Ludger Fink5, 5 Friedrich Grimminger6, Guillermo Barreto1,7, Werner Seeger1,6,8, Soni S. Pullamsetti1,6, Ulf R. 6 Rapp1, Rajkumar Savai1,6,8* 7 Affiliations: 1Max Planck Institute for Heart and Lung Research, Member of the German 8 Center for Lung Research (DZL), Member of the Cardio-Pulmonary Institute (CPI), Bad 9 Nauheim, Germany; 2Institute of Biochemistry I, Goethe University Frankfurt, Frankfurt, 10 Germany; 3Institute of Molecular Oncology, Member of the DZL, Philipps-University 11 Marburg, Marburg, Germany; 4Institute for Genetics; member of the German Center for 12 Lung Research (DZL), Justus-Liebig-University, Giessen, Germany; 5Institute of Pathology and 13 Cytology, UEGP, Wetzlar, Germany; 6Department of Internal Medicine, Member of the DZL, 14 Member of CPI, Justus Liebig University, Giessen, Germany; 7Brain and Lung Epigenetics 15 (BLUE), Glycobiology, Cell Growth and Tissue Repair Research Unit (Gly-CRRET), Université 16 Paris-Est Créteil (UPEC), Créteil, France; 8Institute for Lung Health (ILH), Justus Liebig 17 University, Giessen, Germany; #present address: Department of Biotechnology, Institute of 18 Graduate Studies and Research, Alexandria University, Egypt; §present address: Emmy 19 Noether Research Group Epigenetic Machineries and Cancer, Division of Chronic 20 Inflammation and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany. -
Supplementary Table 1
Supplementary Table 1. 492 genes are unique to 0 h post-heat timepoint. The name, p-value, fold change, location and family of each gene are indicated. Genes were filtered for an absolute value log2 ration 1.5 and a significance value of p ≤ 0.05. Symbol p-value Log Gene Name Location Family Ratio ABCA13 1.87E-02 3.292 ATP-binding cassette, sub-family unknown transporter A (ABC1), member 13 ABCB1 1.93E-02 −1.819 ATP-binding cassette, sub-family Plasma transporter B (MDR/TAP), member 1 Membrane ABCC3 2.83E-02 2.016 ATP-binding cassette, sub-family Plasma transporter C (CFTR/MRP), member 3 Membrane ABHD6 7.79E-03 −2.717 abhydrolase domain containing 6 Cytoplasm enzyme ACAT1 4.10E-02 3.009 acetyl-CoA acetyltransferase 1 Cytoplasm enzyme ACBD4 2.66E-03 1.722 acyl-CoA binding domain unknown other containing 4 ACSL5 1.86E-02 −2.876 acyl-CoA synthetase long-chain Cytoplasm enzyme family member 5 ADAM23 3.33E-02 −3.008 ADAM metallopeptidase domain Plasma peptidase 23 Membrane ADAM29 5.58E-03 3.463 ADAM metallopeptidase domain Plasma peptidase 29 Membrane ADAMTS17 2.67E-04 3.051 ADAM metallopeptidase with Extracellular other thrombospondin type 1 motif, 17 Space ADCYAP1R1 1.20E-02 1.848 adenylate cyclase activating Plasma G-protein polypeptide 1 (pituitary) receptor Membrane coupled type I receptor ADH6 (includes 4.02E-02 −1.845 alcohol dehydrogenase 6 (class Cytoplasm enzyme EG:130) V) AHSA2 1.54E-04 −1.6 AHA1, activator of heat shock unknown other 90kDa protein ATPase homolog 2 (yeast) AK5 3.32E-02 1.658 adenylate kinase 5 Cytoplasm kinase AK7